Comparison of image quality assessment methods for multi-focused image fusion

Image fusion is a process of obtaining one image from multiple. The resulting image carries more information about the photographed scene, than each of the originals. Such an image can be more useful when we deal with human or image processing system. Algorithms that performed this task are used in a wide applying in practical: computer vision, robotics, medicine, forensics, etc. Most popular quality assessment measure for multi-focused image fusion are discribed. Expert image quality assessment experiment was performed. Different kinds of image quality assessment were proposed for scenes with various characteristics

Full-focused image fusion in the presence of noise

The implementation and analysis of the algorithm for the full-focused image fusion in the presence of noise are presented. Three methods of combining noisy images are considered: without pre-processing and post-processing, using prefiltration of original images, using post-filtering of the fused image. The database of test scenes created by the authors was used for testing the proposed algorithm for full-focused image fusion. Additive white Gaussian noise was considered as an noise model. Two-stage digital image processing scheme, based on principal components analysis was used as a filtering algorithm. Quantitative and visual results are shown and demonstrate the main features of the proposed algorithm

Allocation of text characters of automobile license plates on the digital image

Computer vision algorithms become part of our everyday life. Often they care about safety and control of law and order. Now it sufficiently important task in Russia so, the article discuss of development this system to recognize Russian standard car numbers. For successful operation it is necessary to develop computer vision algorithms, adaptive to changing external conditions. Among the large number of such conditions, this article describes only the specific application for automatic license plate recognition. In the article this problem is solved with minimal use of a priori information about the object

Recognition of hand gestures on the video stream based on a statistical algorithm with pre-treatment

The goal of this work is human hand detection and gesture recognition. This is a tremendously difficult task as hands can be very varied in shape and viewpoint, they can de open or closed, they can have different finger articulations. It is proposed a combined method of hand gesture recognition based on a statistical image processing algorithm. As a pre-processing algorithm it was applied Lucas-Kanade method and background subtraction algorithm. Object recognition was performed with using of Haar classifiers. The possibility of using gestures for remote control of various devices with different hands position from the camera location was shown

The LIDAR Odometry in the SLAM

This paper describes an algorithm that performs an contur analyzing of an environment with a single 2D Laser Imaging Detection and Ranging (LIDAR) sensor, as well as its implementation on a mobile platform using the Robot Operating System (ROS). The review of standard sensors shortcomings is provided in article. It is offered decisions on the creation of the space circuit which is based on the result received from a the scanning lazer range finder. Simulation of system is made in the Gazebo environment

Non-reference metrics and its application for distortion compensation

This article summarizes the SNR estimation methods and suggests a relatively simple SNR method for estimating blind signals for QAM and PSK modulation signals. The proposed modification of this method allowed to accurately estimate the BER or SER with a transaction of a relatively small number of symbols and a minimal error without any reference sequences. A practical application of this method is also provided - compensation of the BPSK signal offset using AWGN in the channel

Two-step noise reduction based on soft mask for robust speaker identification

This paper addresses the problem of speaker identification in noisy conditions. A two-step noise reduction algorithm based on soft mask and minimum mean square error short-time spectral amplitude estimator was proposed. It is used in the signal preprocessing stage for more robust speaker identification. The proposed algorithm was tested and compared with the existing noise reduction algorithms in the problem of speaker identification. Testing was carried out with two speech databases and some noise samples from the NOISEX-92 library. The advantage of the new noise reduction algorithm for some noise samples and signal-to-noise ratios was shown

Convolutional Neural Network for Satellite Imagery

Information extracted from aerial photographs has found applications in different areas including urban planning, crop and forest management, disaster relief, and climate modeling. In many cases information extraction is still performed by human experts, making the process slow, costly, and error prone. The goal of this investigation is to develop methods for automatically extracting the locations of objects such as water resource, forest and urban areas from aerial images. We analyze patterns in land using large-scale satellite imagery data which is available worldwide from third-party providers. For training, given the limited availability of standard benchmarks for remotesensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across 300 European cities. The developed algorithms are based on the implementation of a relatively new approach in the field of deep machine learning - a convolutional neural network. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features

Evaluation of face image quality metrics in person identification problem

Face quality assessment algorithms play an important role in improving face recognition accuracy and increasing computational efficiency of biometric systems. In the case of video analysis system, it is very common to acquire multiple face images of a single person. Strategy for optimally choose of the face images with the best quality from the set of images should base on special quality metric. A set of face image quality metrics were investigated: image resolution, sharpness, symmetry, blur, measure of symmetry of landmarks points S, quality measure based on learning to rank. A new metric based on no-reference image quality assessment approach is proposed. For all the metrics the Spearman rank correlation coefficients with subjective expert assessment at different levels of face image scene illumination were calculated. The received results can help computer vison system engineers to optimize the biometric identification system

Landmarks Detection by Contour Analysis in the Problem of SLAM

This paper describes an algorithm that is based on an contur analyzing of an environment with a single 2D Laser Imaging Detection and Ranging (LIDAR) sensor, as well as its implementation on a mobile platform using the Robot Operating System (ROS). The solution is based on landmarks, its mean that all walls in the building could be discribed as a simple objects: angles and lines